System and method for creating a focus-exposure model of a lithography process

ABSTRACT

A system and a method for creating a focus-exposure model of a lithography process are disclosed. The system and the method utilize calibration data along multiple dimensions of parameter variations, in particular within an exposure-defocus process window space. The system and the method provide a unified set of model parameter values that result in better accuracy and robustness of simulations at nominal process conditions, as well as the ability to predict lithographic performance at any point continuously throughout a complete process window area without a need for recalibration at different settings. With a smaller number of measurements required than the prior-art multiple-model calibration, the focus-exposure model provides more predictive and more robust model parameter values that can be used at any location in the process window.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 60/706,144, entitled “Methodology of Unified,Through-Process Window Lithography Modeling.” The subject matter of thisrelated application is hereby incorporated by reference in its entirety.

FIELD OF THE INVENTION

This invention relates generally to optical lithography and moreparticularly to creating a focus-exposure model of a lithographyprocess.

BACKGROUND

The integrated circuit industry has, since its inception, maintained aremarkable growth rate by driving increased device functionality atlower cost. Leading edge devices today provide the computing power ofcomputers that used to occupy entire rooms at a mere fraction of thecost. Many of today's low cost consumer devices include functionalitythat only a few years ago was unavailable at any cost, such as videocell phones, ultra-portable media players, and wireless orultra-wideband Internet devices. One of the primary enabling factors ofthis growth has been the ability of optical lithography processes tosteadily decrease the smallest feature size that can be patterned aspart of the integrated circuit pattern. This steady decline in featuresize and cost while at the same time printing more features per circuitis commonly referred to as “Moore's Law” or the lithography “roadmap.”

The lithography process involves creating a master image on a mask, orreticle, then replicating that pattern faithfully onto the devicewafers. The more times a master pattern is successfully replicatedwithin the design specifications, the lower the cost per finished deviceor “chip.” Until recently, the mask pattern has been an exact duplicateof the desired pattern at the wafer level, with the exception that themask level pattern may be several times larger than the wafer levelpattern. This scale factor is then corrected during wafer exposure bythe reduction ratio of the exposure tool. The mask pattern is typicallyformed by depositing and patterning a light absorbing material on aquartz or other transmissive substrate. The mask is then placed in anexposure tool known as a “stepper” or “scanner” where light of aspecific exposure wavelength is directed through the mask onto thedevice wafers. The light is transmitted through the clear areas of themask and attenuated by a desired amount, typically between 90% and 100%,in the areas that are covered by the absorbing layer. The light thatpasses through some regions of the mask may also be phase-shifted by adesired phase angle, typically an integer fraction of 180 degrees. Afterbeing collected by the exposure tool, the resulting aerial image patternis then focused onto the device wafers. A light sensitive materialdeposited on the wafer surface interacts with the light to form thedesired pattern on the wafer, and the pattern is then transferred intothe underlying layers on the wafer to form functional electricalcircuits according to well known processes.

In recent years, the feature sizes being patterned have becomesignificantly smaller than the wavelength of light used to transfer thepattern. This trend towards “sub-wavelength lithography” has resulted inincreasing difficulty in maintaining adequate process margins in thelithography process. The aerial images created by the mask and exposuretool lose contrast and sharpness as the ratio of feature size towavelength decreases. This ratio is quantified by the k1 factor, definedas the numerical aperture of the exposure tool times the minimum featuresize divided by the wavelength. The lack of sharpness or image blur canbe quantified by the slope of the aerial image at the threshold forimage formation in the resist, a metric known as “edge slope,” or“normalized image log slope,” often abbreviated as “NILS.” The smallerthe NILS value, the more difficult it becomes to replicate the imagefaithfully onto a large number of device patterns with sufficientcontrol to yield economically viable numbers of functional devices. Thegoal of successful “low-k1 lithography” processes is to maintain thehighest NILS possible despite the decreasing k1 value, thereby enablingthe manufacturability of the resulting process.

New methods to increase the NILS in low-k1 lithography have resulted inmaster patterns on the mask that are not exact copies of the final waferlevel pattern. The mask pattern is often adjusted in terms of the sizeof the pattern as a function of pattern density or pitch. Othertechniques involve the addition or subtraction of extra corners on themask pattern (“serifs,” “hammerheads,” and other patterns), and even theaddition of geometries that will not be replicated on the wafer. Inorder to enhance the printability of the intended features, thesenon-printing “assist features” may include scattering bars, holes,rings, checkerboards, or “zebra stripes” to change the background lightintensity (“gray scaling”), and other structures, which are welldocumented in the literature. All of these methods are often referred tocollectively as “Optical Proximity Correction,” or “OPC.”

The mask may also be altered by the addition of phase-shifting regionsthat may or may not be replicated on the wafer. A large variety ofphase-shifting techniques has been described at length in the literatureincluding alternate aperture shifters, double expose masking processes,multiple phase transitions, and attenuating phase-shifting masks. Masksformed by these methods are known as “Phase Shifting Masks,” or “PSMs.”All of these techniques to increase NILS at low-k1, including OPC, PSM,and others, are referred to collectively as “Resolution EnhancementTechnologies,” or “RETs.” The result of all of these RETs, which areoften applied to the mask in various combinations, is that the finalpattern formed at the wafer level is no longer a simple replicate of themask level pattern. In fact, it is becoming impossible to look at themask pattern and simply determine what the final wafer pattern issupposed to look like. This greatly increases the difficulty inverifying that the design data is correct before the mask is made andwafers exposed, as well as verifying that the RETs have been appliedcorrectly and that the mask meets its target specifications.

The cost of manufacturing advanced mask sets is steadily increasing.Currently, the cost has already exceeded one million dollars per maskset for an advanced device. In addition, the turn-around time is alwaysa critical concern. As a result, computer simulations of the lithographyprocess, which assist in reducing both the cost and turn-around time,have become an integral part of semiconductor manufacturing. There are anumber of computer software techniques that address needs in lithographysimulation. For example, there is first-principle-modeling-basedsimulation software that conducts detailed simulation of the physicaland chemical processes. However, such software often runs extremely slowand hence is limited to extremely small areas of a chip design (on theorder of a few square microns). Software tools in this category include“SOLID-C” from Sigma-C (Santa Clara, Calif., USA) and “Prolith” fromKLA-Tencor (San Jose, Calif., USA). Although there is computer softwarethat executes and provides simulation results faster, such software usesempirical models that are calibrated to the experimental data (forexample, “Calibre” from Mentor-Graphics in Wilsonville, Oreg., USA).Even for the “fast” simulation that uses empirical models, a simulationat a full-chip level often requires tens of hours to many days. A new,fast, and accurate approach has been described in U.S. Pat. No.7,003,758, entitled “System and Method for Lithography Simulation,” thesubject matter of which is hereby incorporated by reference in itsentirety, and is referred to herein as the “lithography simulationsystem.”

As illustrated schematically in FIG. 1A, a lithography simulationtypically consists of several functional steps, and thedesign/simulation process resembles a linear flow 100. In step 110, adesign layout that describes the shapes and sizes of patterns thatcorrespond to functional elements of a semiconductor device, such asdiffusion layers, metal traces, contacts, and gates of field-effecttransistors, is created. These patterns represent the “design intent” ofphysical shapes and sizes that need be reproduced on a substrate by thelithography process in order to achieve certain electrical functionalityand specifications of the final device.

As described above, numerous modifications to this design layout arerequired to create the patterns on the mask or reticle used to print thedesired structures. In step 120, a variety of RET methods are applied tothe design layout in order to approximate the design intent in theactually printed patterns. The resulting “post-RET” mask layout differssignificantly from the “pre-RET” design layout created in step 110. Boththe pre- and post-RET layouts may be provided to the simulation systemin a polygon-based hierarchical data file in, e.g., the GDS or the OASISformat.

The actual mask will further differ from the geometrical, idealized, andpolygon-based mask layout because of fundamental physical limitations aswell as imperfections of the mask manufacturing process. Theselimitations and imperfections include, e.g., corner rounding due tofinite spatial resolution of the mask writing tool, possible line-widthbiases or offsets, and proximity effects similar to the effectsexperienced in projection onto the wafer substrate. In step 130, thetrue physical properties of the mask may be approximated in a mask modelto various degrees of complexity. Mask-type specific properties, such asattenuation, phase shifting design, etc., need be captured by the maskmodel. The lithography simulation system described in U.S. Pat. No.7,003,758 may, e.g., utilize an image/pixel-based grayscalerepresentation to describe the actual mask properties.

A central part of lithography simulation is the optical model, whichsimulates the projection and image forming process in the exposure tool.In step 140, an optical model is generated. The optical model needs toincorporate critical parameters of the illumination and projectionsystem: numerical aperture and partial coherence settings, illuminationwavelength, illuminator source shape, and possibly imperfections of thesystem such as optical aberrations or flare. The projection system andvarious optical effects, e.g., high-NA diffraction, scalar or vector,polarization, and thin-film multiple reflection, may be modeled bytransmission cross coefficients (TCCs). The TCCs may be decomposed intoconvolution kernels, using an eigen-series expansion. For computationspeed, the series is usually truncated based on the ranking ofeigen-values, resulting in a finite set of kernels. The more kernels arekept, the less error is introduced by the truncation. The lithographysimulation system described in U.S. Pat. No. 7,003,758 allows foroptical simulations using a very large number of convolution kernelswithout negative impact on computation time and therefore enables highlyaccurate optical modeling. See “Optimized Hardware and Software forFast, Full Chip Simulation,” Y. Cao et al., Proc. SPIE Vol. 5754, 407(2005). While here the mask model generated in step 130 and the opticalmodel generated in step 140 are considered to be separate models, themask model may conceptually also be considered as part of an integratedoptical model.

Further, in order to predict shapes and sizes of structures formed on asubstrate, in step 160 a resist model is used to simulate the effect ofprojection light interacting with the photosensitive resist layer andthe subsequent post-exposure bake (PEB) and development process. Adistinction can be made between first-principle simulation approachesthat attempt to predict three-dimensional resist structures byevaluating the three-dimensional light distribution in resist, as wellas microscopic, physical, or chemical effects such as moleculardiffusion and reaction within that layer. On the other hand, all “fast”simulation approaches that may allow full-chip simulation currentlyrestrict themselves to more empirical resist models that employ as aninput a two-dimensional aerial image provided by the optical model partof the simulator. This separation between the optical model and theresist model being coupled by an aerial image 150 is schematicallyindicated in FIG. 1A. For simplicity, here the fact that the resistmodel may be followed by modeling of further processes, e.g., etch, ionimplantation, or similar steps, is omitted.

Finally, in step 170, the output of the simulation process will provideinformation on the predicted shapes and sizes of printed features on thewafer, such as predicted critical dimensions (CDs) and contours. Suchpredictions allow a quantitative evaluation of the lithographic printingprocess and on whether the process will produce the intended results.

In order to provide the predictive capabilities just mentioned, a numberof fitting parameters that are not known a priori need be found or tunedin a calibration process. Various methods of calibrating lithographymodels have been described in the literature. Generally, thesecalibration methods search for the best overall match between simulatedtest patterns and corresponding test patterns that are printed on actualwafers and measured by a metrology tool, e.g., a CD-SEM or ascatterometry tool.

Accuracy and robustness of the calibration are required to predict CDsof printed patterns, edge placements, and line end placements. Thecalibrated model is in general expected to predict one-dimensional aswell as two-dimensional optical and processing related proximity effectswith sufficient accuracy. It is known that the predictability ofempirical models is mostly limited to a pattern geometry space that hasbeen covered by the shape and size variations of the test or gaugestructures used in the calibration procedure. A current practice andtrend is to include more and more test structure variations to cover aswide and dense a geometry space as practically possible. Typically,thousands of measurement points are utilized for model calibrations.However, currently model calibrations are mostly performed at nominal or“best” optical settings, and therefore only cover the two-dimensionalgeometry space. Extrapolating these models for use when any non-geometryparameters, e.g., optical parameters or lithography process parameters,are changed is difficult.

On the other hand, it is well known that lithographic processesgenerally need to be evaluated by their process window or, moreprecisely, by the common process window of all relevant structures. Thesize of the process window (PW) is commonly measured by an area inexposure-defocus (E-D) space over which variations in the CD or edgeplacement fall within an allowable range. See “The Exposure-DefocusForest,” B. J. Lin, Jpn. J. Appl. Phys. 33, 6756 (1994). Process windowanalysis takes into account that any actual manufacturing process issubject to unavoidable variations of real parameter values, such asexposure dose and focus settings of the lithographic projection system.The common process window of all structures on a device design definesthe process margin, i.e., the tolerance against process parametervariations.

Some recent attempts to predict the through-process window behavior ofOPC models by calibrating the resist model at “best” settings andextrapolating towards variations in dose and defocus have not been verysuccessful, unless separate, discrete model calibrations were performedat different defocus settings. See “High accuracy 65 nm OPCverification: full process window model vs. critical failure ORC,” A.Borjon et al., Proc. SPIE Vol.5754, 1190 (2005). FIG. 1B illustratesmultiple locations covering a process window space, where separate modelcalibrations were performed at each location. In other work, attemptswere made to calibrate models to several focus-exposure-matrix data setsbut only for one-dimensional line-width data. See “Do we need complexresist models for predictive simulation of lithographic processperformance?,” B. Tollkühn et al., Proc. SPIE Vol. 5376, 983 (2004).

In addition, “lumped” parameter models exist, in which the response ofthe system with respect to resist development effects are approximatedby artificially changing the optical model parameters and such modelsmay still be able to be well-calibrated against a set of test patternsat one single process window condition. As another example forillustration, it is well-known that spherical aberration of a projectionsystem causes a pattern-pitch dependent focus shift. Consequently, ifmeasured at a single focus setting, a through-pitch “OPC” curve (whichplots CD versus pitch) will experience a certain modulation due to theoptical effect of spherical aberration. A sufficiently complex resistmodel having a large enough number of adjustable parameters may still beable to reproduce the OPC curve and in fact predict printed CD throughpitch at the exact same focus setting that was used for calibration.However, the ability of the model to extrapolate anywhere outside theimmediate parameter space covered by the calibration would be severelylimited.

There is a constant need for increased accuracy and robustness oflithography modeling. Clearly there is also a need for model calibrationmethodologies that enable predictive modeling in a multidimensionalparameter space, beyond geometry variations but also PW-related processvariations, in order to verify manufacturability of advancedsemiconductor designs by simulation.

SUMMARY

A system and a method for creating a focus-exposure model are introducedfor the calibration of lithography simulation models. The system and themethod utilize calibration data along multiple dimensions of parametervariations, in particular within an exposure-defocus process windowspace. The system and the method provide a unified set of modelparameter values that result in better accuracy and robustness ofsimulations at nominal process condition, as well as the ability topredict lithographic performance at any point throughout a completeprocess window area without a need for recalibration.

In one embodiment, a method for creating a focus-exposure model of alithography process comprises selecting a model of a lithographyprocess, the model including an optical model module, the model having aset of model parameters including focus, exposure, and a set of fittingparameters having variable values, defining a process window for thelithography process in a focus-exposure space, selecting a set ofinitial fitting parameter values for the model, selecting a plurality ofsampling locations within the process window, the plurality of samplinglocations including nominal condition, generating simulated results ofthe lithography process at the plurality of sampling locations withinthe process window using the model with the set of initial fittingparameter values by varying the values of focus and exposure tocorrespond with the plurality of sampling locations while holding theset of initial fitting parameter values constant, comparing thesimulated results with actual results of the lithography process at allof the plurality of sampling locations within the process window toproduce a total difference measure between the simulated results and theactual results, modifying the set of fitting parameter values andgenerating further simulated results at each of the sampling locationswithin the process window to identify optimum fitting parameter valuessuch that the total difference measure between the actual results andsimulated results produced using the optimum fitting parameter values isminimized or is below a predetermined threshold, and defining thefocus-exposure model as the model including the optimum fittingparameter values, the focus-exposure model being capable of simulatingthe lithography process at any location within the entire processwindow.

In one embodiment, a system for generating a single process window modelfor predicting the capability of a photolithography process comprises astorage area for storing information, an input device, an output device,physical model information stored in the storage area, and a modelcalibration module. The storage area is in communication with the modelcalibration module such that selected physical model information can beaccessed by the model calibration module. The input device is incommunication with the model calibration module such that process windowdefinition information defining a process window can be made availableto the model calibration module and such that discrete measurementinformation obtained from measurements of a wafer under different testconditions in the defined process window can be accessed by the modelcalibration module. In addition, the model calibration module isconfigured to generate a single process window model by using theprocess window definition information and the discrete measurementinformation to calibrate the selected physical model information suchthat the performance of a photolithography system over the definedprocess window can be described with two continuously adjustable opticalparameters. Generating the single process window model includescomparing the discrete measurement information with simulatedmeasurements, the simulated measurements produced by simulating thelithography process using the selected physical model information byvarying the two continuously adjustable optical parameters while holdingall other parameters in the physical model information constant.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a flowchart of method steps for a prior art lithographysimulation;

FIG. 1B is a diagram of locations for calibration of multiplelithography simulation models, according to a prior art methodology;

FIG. 2A is a flowchart of method steps for creating a focus-exposuremodel of a lithography process, according to one embodiment of theinvention;

FIG. 2B is a flowchart of method steps for generating a focus-exposuremodel at any arbitrary location in a process window, according to oneembodiment of the invention;

FIG. 3A is a diagram showing one embodiment of areas of samplinglocations in a process window of a lithography process, according to theinvention;

FIG. 3B is a diagram showing one embodiment of sampling locations in aprocess window of a lithography process, according to the invention;

FIG. 4A is a diagram showing another embodiment of sampling locations ina process window of a lithography process, according to the invention;

FIG. 4B is a chart showing sampling locations for calibrating afocus-exposure model, according to one embodiment of the invention;.

FIG. 5 is a chart showing results of calibrations of a focus-exposuremodel using different sampling schemes, according to one embodiment ofthe invention;

FIG. 6 is a chart summarizing a comparison between one embodiment of afocus-exposure model calibration and a prior-art multiple-modelcalibration;

FIG. 7 is a block diagram of one embodiment of a system for creating afocus-exposure model, according to the invention; and

FIG. 8 is a block diagram of one embodiment of a lithography simulationsystem, according to the invention.

DETAILED DESCRIPTION OF THE INVENTION

Disclosed are a system and a method based upon model creation and modelcalibration, which rely upon incorporating data points not only atnominal condition at the center of the process window but also atdifferent locations within the process window at some distance from thecenter. At minimum, calibration data is collected while varying at leastone process parameter (for example, the defocus parameter), and allcollected calibration data with the different values of the variedprocess parameter are utilized for calibration of the model's fittingparameters. The model of the lithography process includes an opticalmodel module, and may optionally include a resist model module, a maskmodel module, and other appropriate model modules. The model modules ofthe model of the lithography process will be referred to herein asmodels, e.g., the optical model and the resist model, for simplicity.

In a preferred implementation, calibration data are collected whilevarying the parameters of exposure dose and defocus to form a processwindow space. The method provides joint fitting of test structures atall values of exposure dose and defocus in order to find a single set ofcalibrated fitting parameters that are predictive of the lithographicprinting process at any location in the process window. The calibrationmethod includes simulating the performance of the lithography process ateach of a set of sampling locations in the process window using a modelof the lithography process including focus and exposure parameters and aset of fitting parameters, where the focus and exposure parameters ofthe optical model are varied in accordance with first principles whilethe fitting parameters (i.e., all other model parameters) are unchanged.The model having the set of calibrated fitting parameter values isreferred to herein as a focus-exposure model (FEM). Focus is a settingof an optical parameter of the exposure tool, and is sometimes alsocalled defocus. The terms focus and defocus are used interchangeablyherein.

A focus-exposure model calibrated at a number of locations throughout afull process window more closely reflects physical reality and is morepredictive, accurate, and robust than a model calibrated only at onenominal process condition. Lithography simulation benefits frommultidimensional model calibration with 1) better model accuracy,predictability, and robustness at nominal or best settings by relying oninterpolation between well-characterized sampling locations in theprocess window, 2) the ability to predict pattern behavior at anyinterpolated location within the process window, and 3) focus-exposuremodel calibration can be achieved with a smaller total number ofmeasurements than calibration of multiple separate models at differentdiscrete locations in the process window.

A key characteristic of the method of calibrating the focus-exposuremodel is the good separability of optical and resist models, in thesense that the optical model in fact captures accurately the trueoptical effects, while resist-related parameters do not change withoptical settings, e.g., through focus settings. Since the defocusbehavior of a printed pattern can be partially compensated by somephysical resist effects, e.g., acid diffusion, without joint calibrationincluding defocus data, there will be multiple root mean square (RMS)minimums in the defocus-resist parameter space. Only one of thoseminimums is physical and correct. Joint calibration throughout theprocess window automatically constrains the model to the correct minimumpoints since the trend behavior with defocus is orthogonal to the resistbehaviors. Therefore, false “parameter lumping” effects are avoided, andthe resulting calibrated model will be more accurate and robust even atnominal exposure conditions. In other words, even at nominal processconditions, the calibrated focus-exposure model will be able to predictthe pattern behavior better than a model calibrated only at nominalprocess conditions.

Although “the process window” is most frequently defined in atwo-dimensional exposure-defocus space, the method described herein isnot restricted to this definition. This definition inherently reflectsthat focus and dose variations usually have a dominant impact onlithography process performance. It is possible, however, to generalizethe process window concept by using more, or different, parameterdimensions that may be adjusted or varied. Such a generalization willhelp to capture process margin with respect to these additionalparameter variations and may also add further constraints to the modelfitting. Additional constraints help make the calibrated model morephysical, and hence more accurate and predictive. For example, in amodern exposure tool, many optical settings are adjustable to someextent, including, for example, illumination wavelength or line-width,lens settings and thereby optical aberrations, and a wide range ofilluminator adjustments. Similarly, parameters relating directly toresist layer properties may be varied or adjusted. Any of these orsimilar parameters may be included in model calibration for the benefitof improved model robustness or accuracy. The calibration process mayalso utilize test data from a set of nominally identical exposure toolsin a production environment.

FIG. 2A is a flowchart of method steps 200 for creating a focus-exposuremodel of a lithography process, according to one embodiment of theinvention. In step 212, a set of test patterns that will be manufacturedon a test mask and printed onto test wafers using a lithography exposuretool are defined. These test patterns need to cover a full range ofproximity interactions that are characteristic of the lithographyprocess under consideration. A wide range of line/space patterns withvarying pitches (from isolated to dense), and two-dimensional patternssuch as line/space ends with varying gap sizes should be included. Theline/space patterns span over a one-dimensional spatial frequency spacewhile the line end patterns cover two-dimensional effects, in particularline-end pull back, pinching, etc. It is also possible to define atwo-dimensional space by “pattern curvature” and construct test patternsaccordingly, or use more complex two-dimensional test patterns that arerepresentative of typical shapes found in the designs that thelithography model will be used on.

Given the importance of separating optical and resist effects, asdiscussed above, it may also be possible to enhance the calibration byinclusion of pattern types that are more sensitive to certain effectsthan others. A possible example might be test patterns that areparticularly sensitive to optical effects such as particular opticalaberrations or stray light (flare) if these effects are incorporated inthe optical model of the simulation tool. It is well-known that certainpattern types show particular sensitivities to optical effects, forexample, line pairs to coma and brick-wall patterns to three-foil. Suchoptical aberration or flare test patterns, possibly in combination withcorresponding process variations may further improve model separationand calibration performance. Generally, particular pattern types maycorrelate specifically to particular model parameters. These patterntypes can, e.g., be identified by a sensitivity analysis and may begiven corresponding weights during the model optimization.

Another method of taking into account the optical aberrations in modelcalibration is to directly use the optical aberrations measuredseparately using tools specifically designed for optical aberrationmeasurement. Some examples of the optical aberration measurement toolsinclude those on-scanner-stage self-metrology tools provided by scannervendors, such as ILIAS by ASML and other tools by Litel Corp. In thiscase, there is no need to include optical aberration-sensitive testpatterns in the model calibration. The optical aberration parameters arenot adjustable parameters to be calibrated during model calibration, butare fixed as known parameters in the optical model. A typical example offixed optical parameters is the source map, i.e., the exact gray-scaleshape and value of the illuminator, which is often measured and providedas a known entity not to be adjusted during model calibration. For anyknown optical parameters (e.g., source map, optical aberrations, andpupil shape), they are treated as fixed optical parameters in theoptical model during model calibration.

After the set of test patterns has been defined, in step 214 a processwindow is defined and locations in the process window are selected forthe calibrations. The process window is defined by selecting processconditions that will be varied for the model calibration and the rangeof these variations. For a typical application, an exposure-defocusspace that approximately matches, or exceeds, the expected processwindow will be covered by sampling locations. This coverage isillustrated in FIG. 3A, which shows five areas 312, 314, 316, 318, and320 of sampling locations in an exposure-defocus space 300. FIG. 3Bshows one embodiment of an expected process window 322 in anexposure-defocus (E-D) space 320 and five sampling locations 324, 326,328, 330, and 332, which include nominal or best conditions in thecenter 332 of process window 322 as well as four additional samplinglocations 324, 326, 328, and 330 close to the boundary thereof. Inpractice, greater or fewer than five sampling locations may be used.

For each sampling location 324-332, the set of test patterns defined instep 212 is used to generate fitting parameters. Good fitting can beobtained with a reduced number of pattern types at some of the samplinglocations. A complete set of test patterns, typically on the order ofseveral thousands, may be measured at center 332 of process window 322,while a significantly reduced number of patterns, e.g., 10-20 percent ofall, are utilized at sampling locations 324-330 at the periphery ofprocess window 322. As a consequence, the total number of test patternmeasurements required for focus-exposure model calibration issignificantly smaller than the multiple-model calibration doneseparately for each individual sampling location, which is currentlyrequired by other existing calibration methods.

As has been noted before, the exposure-defocus space shown in FIGS. 3Aand 3B may typically be used as a basis for the multidimensionalcalibration, but alternative and additional parameter dimensions may beused as well in an equivalent manner.

Returning to FIG. 2A, in step 216 the defined set of test patterns andprocess conditions is analyzed to ensure that a relevant parameter spaceis sufficiently well covered. Implementation of step 216 may involve atwo-dimensional frequency space analysis of the test patterns, analysisof estimated process windows using nominal, best-effort, or defaultsimulation parameters, or other methods. If the defined test patternsand process conditions do not provide sufficient coverage of theparameter space, in step 218 additional test patterns or locations inthe process window are defined. The method then returns to step 216.While the analysis of step 216 may be considered as an integral part ofthe pattern and parameter selection, it is shown as a separate step inFIG. 2A to highlight the importance of this consideration. It has beenshown in the literature that adding more test patterns to a calibrationdoes not automatically improve accuracy, unless the additional datapoints provide a more complete coverage of the relevant parameter space.

If the defined test patterns and process conditions provide sufficientcoverage of the parameter space, the method continues with step 220. Instep 220, for applications in optical projection lithography, which isthe current state-of-the-art in semiconductor manufacturing, a test maskis fabricated using the same mask technology and manufacturing method aswill be used in the lithography process that is to be described by thecalibrated focus-exposure model. The test mask contains the whole set ofpreviously defined test patterns. It may for example be a binary mask,an attenuated phase-shift mask, or an alternating phase-shift orchromeless phase-shift mask, depending on the lithographic process underconsideration. For future lithography, optical maskless technologies maybe introduced, e.g., using spatial light modulators instead of a fixed,invariable template reticle. The focus-exposure model calibration willbe equally applicable and beneficial for these technologies, where thestep of mask fabrication can be considered a virtual one. In OpticalMaskless Lithography (OML), the spatial light modulator settingscorresponding to the selected test patterns (computed by any selectedrasterization algorithm for the OML system) are used in place of thetest mask.

In step 222, using the test mask, test wafers are printed in an exposuretool that is representative of the lithography process underconsideration and using identical resist parameters and processingconditions as the device manufacturing process. This printing processwill include application of a resist layer on the wafers, typicallyhaving one or more additional anti-reflection layers, a pre-exposurebake step, exposure in a scanner or stepper by projecting an image fromthe test mask onto the resist-coated wafer, a post exposure bake step,and resist development. The printing process may also include anadditional step of etching the wafer, if such is also part of thesimulation model. Printing of test patterns is performed using allprocess settings previously defined as sampling locations in the processwindow. Printing the test patterns for all of the sampling locations maybe achieved by varying parameters stepwise between repeated exposures ona single substrate or by exposing a number of test wafers separately.

In step 224, the test patterns on the fully processed wafers aremeasured using suitable metrology tools to generate actual results. Step224 may include line-width and line end-pullback measurements using aCD-SEM or CD-AFM, generation and analysis of two-dimensional SEM imagedata, optical scatterometry analysis of CDs, or other measurements thatcan be correlated to predicted pattern parameters derived from thefocus-exposure model.

The actual results derived from the printed test patterns need to bematched by simulated test patterns from a selected model of thelithography process. The model of the lithography process includes oneor more model modules representing the lithography process. The modelincludes at least an optical model, and may optionally include a resistmodel, a mask model, and any other appropriate models when applicable,e.g., an etch model after the resist model. In the method of FIG. 2A,the model only includes an optical model and a resist model for ease ofillustration. In step 226, initial fitting parameter values for anoptical model are selected and in step 228, initial fitting parametervalues for a resist model are selected. The initial fitting parametervalues for the optical model and the resist model may be nominal,default, or best-guess values for the fitting parameters. For theoptical model, the fitting parameters are all the adjustable parametersof the optical model. The exposure dose and defocus are not consideredas adjustable parameters but will be varied in accordance with firstprinciples to correspond to the selected sampling locations in theprocess window. In step 230, the printed test patterns are simulatedusing the optical model and the resist model. In the preferredembodiment, the simulation of step 230 is implemented using the systemand method disclosed in U.S. Pat. No. 7,003,758. In one embodiment, alithography simulation system 800, described below in conjunction withFIG. 8, is utilized to perform step 230. In step 230, simulations areperformed for all test patterns and for all locations in the processwindow defined in steps 212-216 to produce simulated results. During thesimulations, the exposure dose and defocus parameters of the model arevaried in accordance with first principles and the values of the fittingparameters of the model, including all of the fitting parameters of theresist model, remain unchanged.

Next, in step 232, the pattern parameters of the simulated results arethen compared against the actual results, e.g., by comparing simulatedline/space or gap widths to corresponding CD-SEM measurements.Alternatively, “measurements” may be performed on simulated resist (oretched) contour lines that represent the predicted two-dimensionalprinted patterns, and these measurements on simulated patterns arecompared against the equivalent measurements of printed patterns. Themeasurements may involve scalar values such as CD or line-end pull back,edge placement errors, or more complex evaluations of correspondingtwo-dimensional shapes. The CD measurement is used in the discussionbelow for illustrative purposes, and measurements of any other patternparameters may be used in a similar fashion and are within the scope ofthe invention.

To quantify the agreement between the simulated results and the actualresults, in step 232 a difference measure between the printed testpatterns and the simulated test patterns is calculated for each samplinglocation in the process window. In one embodiment, the differencemeasure is represented by a cost function that can be calculated toreflect the “distance” between simulated and measured values in aRoot-Mean-Square (RMS) sense as defined below in Equation 1. In Equation1, RMS(k) is the “distance” between the simulated and measured CD valuesafter the k-th iteration of the cost function, M is the total number ofsampling locations in the process window, N is the number of testpatterns to be measured at each sampling location in the process window,CD_(meas)(E_(i), F_(i), TP_(j)) is the actual CD measurement at the j-thtest pattern (TP) fabricated with the focus and exposure values at thei-th sampling location in the process window (E_(i), F_(i)), where E isan exposure dose value and F is a focus value, and CD_(simu)(E_(i),F_(i), TP_(j), V _(k)) is the simulated CD measurement of thecorresponding test pattern using the focus and exposure values at thei-th sampling location in the process window, where V _(k) is a set offitting parameters V _(k)=(v₁ ^(k), v₂ ^(k), . . . , v_(L) ^(k)), whereL is the total number of fitting parameters of the optical model and theresist model and k indicates the adjusted fitting parameters after thek-th iteration. The definition of the cost function may includedifferent weight factors, W_(ij), for various data points or otheradjustments. $\begin{matrix}{{{RMS}(k)} = \sqrt{\frac{1}{M \times N}{\sum\limits_{i = 1}^{M}\quad{\sum\limits_{j = 1}^{N}\quad{W_{i,j}\begin{bmatrix}( {{{CD}_{meas}( {E_{i},F_{i},{TP}_{j}} )} -}  \\{{CD}_{simu}( {E_{i},F_{i},{TP}_{j},{\overset{harpoonup}{V}}_{k}} )}\end{bmatrix}}^{2}}}}} & ( {{Eq}.\quad 1} )\end{matrix}$

The cost function value calculated by Equation 1 is called the RMSdifference between the simulated results and the actual results, and inone embodiment is used as the difference measure in step 232. Themagnitude of the cost function is a measure of the quality of thefitting between the simulated results and the actual results, and thegoal of the calibration process is to optimize the focus-exposure modelby varying the adjustable fitting parameters to minimize the costfunction, e.g., the RMS(k), as shown in Equation 2.MIN=Minimize(RMS(k)), k=1,2,   (Eq. 2)

In step 234, a determination is made whether the calculated differencemeasure is below a predetermined threshold. Alternatively, a globalminimum of the difference measure is sought. If the difference measureis not minimized or below the predetermined threshold, the methodcontinues in step 236, in which fitting parameter values of the opticalmodel module and the resist model module are adjusted or tuned in acertain sequence. The method then returns to step 230 to simulate theprinted test patterns using the adjusted fitting parameter values forthe optical model and the resist model. Then in step 232, a differencemeasure between the new simulated test patterns and the printed testpatterns is calculated and the difference measure is evaluated in step234. Steps 236, 230, 232, and 234 are repeated until the currentdifference measure is minimized or below the predetermined threshold.

Then, in step 238, the current fitting parameter values of the opticalmodel and the resist model are designated as the fitting parametervalues for the calibrated focus-exposure model. The calibratedfocus-exposure model can then be used to simulate the lithographyprocess at any location within the process window.

A key characteristic of the calibration of the focus-exposure model isthe inclusion of data points along several dimensions of processparameters during the simulation of the test patterns, typicallyincluding several process settings in an exposure-defocus process windowspace, while corresponding constraints are placed on fitting parametervalues during the calibration procedure. This simply means that only theprocess conditions in the optical model that have actually been adjustedin the test wafer printing process between sampling locations areallowed to change in accordance with first principles in the simulationsof the test patterns at the sampling locations, e.g., focus and exposuredose in the method of FIG. 2A, and all other fitting parameters of themodel are held constant between the sampling locations in the processwindow. A single, universal, set of model parameter values is thusderived from the calibration process that can be used to generate “new”models (i.e., predicted patterns) at any exposure-dose setting within areasonable vicinity of the initial sampling area in the process window,and not just at the exact locations used for calibration. Even if alithography process will only be simulated at nominal condition, betterperformance is achieved when the focus-exposure model is calibratedusing data collected not only at the center of the process window but atmultiple locations some distance from the center of the process window.

FIG. 2B is a flowchart of method steps for generating a model at anylocation in a process window, according to one embodiment of theinvention. In step 252, the focus-exposure model is calibrated accordingto the method described above in conjunction with FIG. 2A. In step 254,a location in the process window where the lithography process is to besimulated is selected. The selected location can be anywhere in theprocess window, i.e., the selected location can be but is not requiredto be one of the sampling locations used during the calibration of thefocus-exposure model. Then, in step 256, a model is generated byapplying the set of values for the varied model parameters (e.g.,exposure and focus) that corresponds to the selected location in theprocess window to the calibrated focus-exposure model in accordance withfirst principles while all of the other calibrated model parameters(i.e., fitting parameters) are held at the final fitted values of theFEM. The model may then be used to simulate the performance of thelithography process at the selected location in the process window.

An exemplary calibration of a focus-exposure model for a 65 nmlithography process was performed. A total set of approximately 2000one-dimensional and two-dimensional test patterns were defined for modelcalibration in this 65 nm process. Eleven locations within a processwindow were selected for the calibration. These locations are shownschematically in FIG. 4A. Test wafers were printed for these elevenlocations in the process window. As shown in FIG. 4B, these locationsinclude focus offsets of +/%31 100 nm and +/−150 nm, as well as exposurevariations of +/−2.41% to +/−4.82% from nominal values. Severalcalibration runs were performed for different subsets of the elevenlocations shown in FIG. 4B. In the cases where fewer than all elevenlocations were used for calibration, the remaining data was used formodel verification by determining the deviation of the simulated fromthe measured test parameter values. The full set of approximately 2000test patterns were used at nominal condition (the center of the processwindow), while at all other sampling locations only 300, i.e., 15%, ofthe test patterns were included. All measurements were scalar CDmeasurements, and the accuracy of the model is quantified by the RMSdeviation between simulated and measured CD values.

FIG. 5 shows the results of the calibration runs. The second columngives a graphical representation of the sampling locations in theprocess window used for calibration; data from all remaining sites wasused for model verification. The points shown in each cell of the secondcolumn correspond to the equivalent locations shown in FIG. 4B. Thenumber of calibration and verification sites is given in column 3 and 4of FIG. 5, respectively. Column 5 lists the total RMS in nm over all thesites and all patterns, while the maximum RMS at any single samplinglocation is given in the final column. The numbers indicate that whilethe best overall fitting is obtained when using data from all 11sampling locations, there is only very minor degradation of the fitquality even after reducing the number of sampling locations to only 3along the defocus direction. Therefore, it is a preferred best practiceto calibrate the focus-exposure model using data collected at only threesampling locations in the process window: nominal condition, a positivedefocus condition at nominal exposure, and a negative defocus conditionat nominal exposure. Also, with the exception of the exposure only case,where sampling locations with exposure variations only were chosen, allother results indicate that the model accuracy is not sensitive to theexact selection of sampling locations in the process window. The methodfor creating a calibrated focus-exposure model even allows for amoderate extrapolation outside the parameter range covered by thecalibration data.

In the exposure only case, where no defocus data was included in thecalibration, and only small variations in dose, the parameter fittingprocess resulted in wrong optical parameters. This result is not toosurprising. As has been discussed before, defocus effects may bemimicked by resist parameters, e.g., diffusion constants, and withoutthrough-focus data the fitting is not sufficiently constrained togenerate accurate model parameter values. Therefore, a criterion forselecting off-nominal sampling locations for model calibration is toinclude at least one sampling location off the nominal focus to obtainaccurate final values for the fitting parameters.

The method of the invention provides significant benefits over thecurrent practice of calibrating models separately for different discretepoints in a process window. FIG. 6 provides a comparison between the twocalibration approaches, i.e., calibration of the focus-exposure modeland calibration of multiple discrete models, respectively. In FIG. 6, itis assumed that each of the sampling locations requires N measurementsand that the number of the extra sampling locations in addition to thenominal sampling location is x. In the prior art multiple discretemodel, the total number of measurements required for all (1+x) locationsis therefore (1+x) N. In contrast, because as mentioned above thefocus-exposure model requires only 15% of the measurements at the extra(i.e., non-nominal) sampling locations, the total number of measurementsrequired for all (1+x) locations becomes only (1+0.15 x) N. Also, whilethe prior art multiple discrete model requires separate calibration ateach of the (1+x) locations, the focus-exposure model requires only onecalibration with measurements at all locations in the process windowconsidered simultaneously. In addition, unlike the prior art multiplediscrete model, the focus-exposure model has separable common mask,optical, and resist terms. Furthermore, the focus-exposure model iscapable of generating unlimited additional models within the entireboundary defined by the sampling locations (i.e., producing accuratepredictions at an unlimited number of locations within the processwindow) as shown in FIG. 2B, while the prior art multiple discrete modelcan be accurate only at the (1+x) locations where separate calibrationshave been conducted.

FIG. 7 is a block diagram of one embodiment of a system 700 for creatinga focus-exposure model of a lithography process, according to theinvention. System 700 includes, but is not limited to, an input device712, a model calibration module 714, an output device 716, and a storagearea 718. Storage area 718 includes, but is not limited to, physicalmodel information 720. Physical model information 720 includes, but isnot limited to, optical model information 722 and resist modelinformation 724. Optical model information 722 includes an optical modeland a set of possible values for each optical model parameter, andresist model information 724 includes a resist model and a set ofpossible values for each resist model parameter. Model calibrationmodule 714 receives process window definition information and printedtest pattern measurements via input device 712. Model calibration module714 uses the process window definition information and the printed testpattern measurements in conjunction with optical model information 722and resist model information 724 to generate a calibrated focus-exposuremodel. The calibrated focus-exposure model is capable of describing theperformance of a lithography process over a process window described byat least two continuously adjustable optical parameters.

FIG. 8 is a diagram of one embodiment of a lithography simulation system800, according to the invention. System 800 includes, but is not limitedto, one or more general purpose-type computing systems, including butnot limited to, an application processing system 814 a and a front-endprocessing system 814 b. Application processing system 814 a is suitablyconfigured to handle job management of the overall operations of system800. In particular, in one embodiment, application processing system 814a includes an application processing device 836 and an application SCSIRAID 838 a. Application processing device 836 is suitably programmed toprovide management of the operations of the various components of system800. In this regard, for example, application processing device 836 maybe programmed to partition a design database for the various componentsof an accelerator system 816, thereby specifying the individual jobs,functions or processes performed by components of accelerator system816. Application SCSI RAID hard-disk array 838 a provides storage forthe programs and data (for example, design database) used by applicationprocessing device 836.

Front-end processing system 814 b includes a front-end processing device840 which is suitably programmed to handle or perform direct interactionwith the user or operator (i.e., the “outside world”) via, for example,client computer(s) (not illustrated) that provide operator or useraccess to system 800 for job setup and/or results review/analysis. Afront-end SCSI RAID hard-disk array 838 b, associated with front-endprocessing device 840 should be a high capacity storage device, sincefront-end SCSI RAID 838 b is used to store results and images of manysimulation jobs. Front-end processing system 814 b also communicateswith application processing system 814 a, to provide or retrieve data toor from application SCSI RAID 838 a (for example, the design database),and instructs application processing system 814 a to start a job, asinstructed by the user or operator.

Application processing system 814 a and front-end processing system 814b connect with accelerator system 816, for example, through high speedswitches (for example, gigabit-Ethernet switches 842 a and 842 b).Switches 842 a and 842 b may be Dell 5224 Power Connect, manufacturedand provided by Dell Computer (Austin, Tex., USA). The implementationand operation of the Dell 5224 Power Connect are described in detail inapplication notes, technical/journal articles and data sheets, all ofwhich are incorporated by reference herein.

In one embodiment, all or substantially all of the actualcomputationally intensive tasks of lithography simulation may beconducted by accelerator system 816, and, in particular, one or moreaccelerator components 816 a-n. This architecture allows scalablecomputation capacity, by changing the number of accelerator hardwarecomponents 816 a-n. Moreover, this architecture also enables or enhancesoverall fault-tolerance of system 800. For example, should a givenaccelerator hardware component 816 a-n fail, its jobs may be re-assignedto the other accelerator hardware components 816 a-n, and, in this way,system 800 maintains its operational condition/state.

In particular, accelerator system 816 may include one or moreaccelerator components 816 a-n, each having one of microprocessorsubsystem 844 a-n (including one or more microprocessors), one or moreaccelerator subsystems 846 a-n, and local or resident memory storage 848a-n coupled to an associated microprocessor subsystem 844 a-n. Theextent or amount of hardware acceleration capability may be balancedwith microprocessor subsystems 844 a-n, depending on the extent oramount of computation to be performed.

In one embodiment, microprocessor subsystems 844 a-n each includes twoXeon microprocessors manufactured by Intel (Santa Clara, Calif., USA).The accelerator subsystems 846 a-n each includes a plurality ofApplication-Specific Integrated Circuit (ASIC), special-purpose DSPintegrated circuits, and/or programmable gate arrays (for example,field-programmable gate arrays (“FPGAs”)). In fact, each of acceleratorsubsystems 846 a-n may include multiple accelerator subsystems, forexample, accelerator subsystem 846 a may include all the acceleratorsubsystems 846 a 1-6 ax, as illustrated in FIG. 8. In this way, whenfully utilized, each of accelerator subsystems 846 a-n containscomputational capacity of roughly twenty-five Xeon microprocessors.

A bus 850 a-n facilitates high-speed communication betweenmicroprocessor subsystem 844 a-n and associated accelerator subsystem(s)846 a-n. The communication protocols and techniques on bus 850 a-n maybe PCI, PCIX, or other high-speed communication protocols andtechniques. Indeed, any high-speed technique, whether now known or laterdeveloped, may be implemented over bus 850 a-n. Notably, in oneembodiment, the bus interface may be implemented using a 21P100BGC PCI-Xbridge (64 bit/133 MHz) from International Business Machines Corporation(Armonk, N.Y., USA). The implementation and operation of the 21 P100BGCare described in detail in application notes, technical/journal articlesand data sheets, all of which are incorporated by reference herein.

The invention has been described above with reference to specificembodiments. It will, however, be evident that various modifications andchanges may be made thereto without departing from the broader spiritand scope of the invention as set forth in the appended claims. Theforegoing description and drawings are, accordingly, to be regarded inan illustrative rather than a restrictive sense.

1. A method for creating a focus-exposure model of a lithographyprocess, comprising: selecting a model of a lithography process, themodel including an optical model module, the model having a set of modelparameters including focus, exposure, and a set of fitting parametershaving variable values; defining a process window for the lithographyprocess in a focus-exposure space; selecting a set of initial fittingparameter values for the model; selecting a plurality of samplinglocations within the process window, the plurality of sampling locationsincluding nominal condition and being a subset of all possible processconditions within the process window; generating simulated results ofthe lithography process at each of the plurality of sampling locationswithin the process window using the model with the set of initialfitting parameter values by simulating the lithography process withvarying values of focus and exposure corresponding to the plurality ofsampling locations within the process window while holding constant theinitial fitting parameter values; comparing the simulated results withactual results of the lithography process at each of the plurality ofsampling locations within the process window to produce a totaldifference measure between the simulated results and the actual resultsat all of the plurality of sampling locations; modifying the set offitting parameter values and generating additional simulated results ateach of the plurality of sampling locations within the process window toidentify optimum fitting parameter values such that the total differencemeasure between the actual results and simulated results produced usingthe optimum fitting parameter values is minimized or is below apredetermined threshold; and defining the focus-exposure model as themodel including the optimum fitting parameter values, the focus-exposuremodel being capable of simulating the lithography process at anylocation within the entire process window.
 2. The method of claim 1,wherein the focus-exposure model is used to simulate the lithographyprocess at a single location within the process window.
 3. The method ofclaim 1, wherein the focus-exposure model is used to simulate thelithography process at a location within the process window that is notone of the plurality of sampling locations by applying focus andexposure values corresponding to the location within the process windowto the focus-exposure model in accordance with first principles withoutchanging the optimum fitting parameter values.
 4. The method of claim 1,wherein the set of model parameters further includes one or more firstprinciple parameters in addition to focus and exposure.
 5. The method ofclaim 4, wherein the one or more first principle parameters includes oneor more of illumination source, numerical aperture, and opticalaberrations.
 6. The method of claim 1, wherein the model of thelithography process further includes a resist model module.
 7. Themethod of claim 1, wherein the model of the lithography process furtherincludes a mask model module.
 8. The method of claim 1, wherein theplurality of sampling locations include only sampling locations atnominal exposure and varying values of focus.
 9. The method of claim 1,wherein the plurality of sampling locations includes only nominalcondition, a positive defocus condition at nominal exposure condition,and a negative defocus condition at nominal exposure condition.
 10. Themethod of claim 1, further comprising: selecting a set of test patternsfor a test mask, wherein the set of test patterns covers a full range ofproximity interactions that are characteristic of the lithographyprocess; printing the set of test patterns on a wafer to form a set oftest structures; and using the set of test structures to produce theactual results.
 11. The method of claim 1, wherein the simulated resultsand the actual results are critical dimension measurements.
 12. Themethod of claim 1, wherein the total difference measure is a root meansquare difference.
 13. A method for creating a focus-exposure model of alithography process, comprising: selecting a set of process conditionswithin a predetermined process window of a lithography process, the setof process conditions being a subset of all possible process conditionswithin the process window, each process condition being an exposurevalue and a defocus value; selecting a model of the lithography process,the model including an optical model module, the model having a set ofmodel parameters including focus, exposure, and a set of fittingparameters having variable values; simulating the lithography process ateach of the set of process conditions using the model to producesimulated results, wherein values of the focus and exposure parametersare varied to correspond to the set of process conditions and thefitting parameter values are held constant; and calibrating the model bycomparing the simulated results with actual results of the lithographyprocess at all of the set of process conditions to produce a singlefocus-exposure model capable of simulating the lithography process atall possible process conditions within the predetermined process window.14. The method of claim 13, wherein the focus-exposure model is used tosimulate the lithography process at a process condition within thepredetermined process window that is not one of the set of processconditions.
 15. The method of claim 13, wherein the set of modelparameters further includes one or more first principle parameters inaddition to focus and exposure.
 16. The method of claim 15, wherein theone or more first principle parameters includes one or more ofillumination source, numerical aperture, and optical aberrations. 17.The method of claim 13, wherein the model of the lithography processfurther includes a resist model module.
 18. The method of claim 13,wherein the model of the lithography process further includes a maskmodel module.
 19. The method of claim 13, wherein the set of processconditions includes only process conditions at nominal exposure andvarying values of focus.
 20. The method of claim 13, wherein the set ofprocess conditions includes only nominal condition, a positive defocuscondition at nominal exposure condition, and a negative defocuscondition at nominal exposure condition.
 21. The method of claim 13,further comprising: selecting a set of test patterns for a test mask,wherein the set of test patterns covers a full range of proximityinteractions that are characteristic of the lithography process;printing the set of test patterns on a wafer to form a set of teststructures; and using the set of test structures to produce the actualresults.
 22. The method of claim 13, wherein the simulated results andthe actual results are critical dimension measurements.
 23. A method forgenerating a focus-exposure model of a lithography process capable ofsimulating the lithography process over an entire process window,comprising: obtaining measurements of a set of test structures printedon a wafer using the lithography process at each of a set of processconditions, the set of process conditions being a subset of all possibleprocess conditions within a process window in an exposure-defocus space;simulating the lithography process at each of the set of processconditions using a model of the lithography process to produce simulatedresults, the model including model parameters including focus, exposure,and a set of fitting parameters having variable values; determiningoptimum values of the fitting parameters that produce simulated resultsthat are a best fit with the measurements of the set of test structuresat all of the set of process conditions; and defining the focus-exposuremodel as the model having the optimum values of the fitting parameters.24. The method of claim 23, wherein the focus-exposure model is used tosimulate the lithography process at a process condition within theprocess window that is not one of the set of process conditions byapplying focus and exposure values corresponding to the processcondition within the process window to the focus-exposure model inaccordance with first principles without changing the optimum values ofthe fitting parameters.
 25. The method of claim 23, wherein the modelparameters include one or more first principle parameters in addition tofocus and exposure.
 26. The method of claim 25, wherein the one or morefirst principle parameters includes one or more of illumination source,numerical aperture, and optical aberrations.
 27. The method of claim 23,wherein the model of the lithography process includes a resist modelmodule.
 28. The method of claim 23, wherein the model of the lithographyprocess includes a mask model module.
 29. The method of claim 23,wherein the set of process conditions include only process conditions atnominal exposure and varying values of focus.
 30. The method of claim23, wherein the set of process conditions include only nominalcondition, a positive defocus condition at nominal exposure condition,and a negative defocus condition at nominal exposure condition.
 31. Themethod of claim 23, further comprising: selecting a set of test patternsfor a test mask, wherein the set of test patterns covers a full range ofproximity interactions that are characteristic of the lithographyprocess; and printing the set of test patterns on the wafer to producethe set of test structures.
 32. The method of claim 23, wherein themeasurements of the set of test structures and the simulated results arecritical dimension measurements.
 33. A system for generating a singleprocess window model for predicting the capability of a lithographyprocess, comprising: a storage area for storing information; an inputdevice; an output device; physical model information stored in thestorage area; and a model calibration module; the storage area being incommunication with the model calibration module such that selectedphysical model information can be accessed by the model calibrationmodule; the input device being in communication with the modelcalibration module such that process window definition informationdefining a process window can be made available to the model calibrationmodule and such that discrete measurement information obtained frommeasurements of a wafer under different test conditions in the definedprocess window can be accessed by the model calibration module, and themodel calibration module is configured to generate a single processwindow model by using the process window definition information and thediscrete measurement information to calibrate the selected physicalmodel information such that the performance of a lithography processover the defined process window can be described with two continuouslyadjustable optical parameters, wherein generating the single processwindow model includes comparing the discrete measurement informationwith simulated measurements, the simulated measurements produced bysimulating the lithography process using the selected physical modelinformation by varying the two continuously adjustable opticalparameters while holding all other parameters in the physical modelinformation constant.
 34. The system of claim 33, wherein the twocontinuously adjustable optical parameters are focus and exposure. 35.The system of claim 34, wherein the simulated measurements are producedusing values of the two continuously adjustable optical parameters onlyat nominal exposure and varying values of focus.
 36. The system of claim34, wherein the simulated measurements are produced using values of thetwo continuously adjustable optical parameters only at nominalcondition, a positive defocus condition at nominal exposure condition,and a negative defocus condition at nominal exposure condition.
 37. Thesystem of claim 33, wherein the physical model information includes oneor more of illumination source, numerical aperture, and opticalaberrations.
 38. The system of claim 33, wherein the physical modelinformation includes resist model information.
 39. The system of claim33, wherein the physical model information includes mask modelinformation.
 40. A method for creating a model of a lithography process,comprising: selecting a set of process conditions within a predeterminedprocess window of a lithography process, the set of process conditionsbeing a subset of all possible process conditions within the processwindow, each process condition being a value for at least one parameter;selecting a model of the lithography process, the model having a set ofmodel parameters including the at least one parameter of the processcondition and a set of fitting parameters; simulating the lithographyprocess at each of the set of process conditions using the model toproduce simulated results, wherein the value of the at least oneparameter is varied to correspond to the set of process conditions whilethe fitting parameter values are held constant; and calibrating themodel by comparing the simulated results with actual results of thelithography process at all of the set of process conditions to produce asingle model capable of simulating the lithography process at allpossible process conditions within the predetermined process window. 41.The method of claim 40, wherein the at least one parameter is an opticalparameter.
 42. The method of claim 41, wherein the optical parameter isfocus.
 43. The method of claim 41, wherein the optical parameter is anumerical aperture of a lithography exposure tool.
 44. The method ofclaim 40, wherein the at least one parameter is a resist parameter. 45.The method of claim 40, wherein the at least one parameter includes anoptical parameter and a resist parameter.
 46. A method for creating asingle model of a lithography process for use at nominal condition,comprising: selecting a set of process conditions within a predeterminedprocess window of a lithography process, the set of process conditionsbeing a subset of all possible process conditions within thepredetermined process window, the set of process conditions includingnominal condition, each process condition being a value for at least oneparameter; selecting a model of the lithography process having modelparameters including the at least one parameter of the process conditionand a set of fitting parameters; simulating the lithography process ateach of the set of process conditions using the model to producesimulated results, wherein the value of the at least one parameter isvaried to correspond to the set of process conditions while the fittingparameter values are held constant; and calibrating the model byminimizing a total difference measure between the simulated results andactual results of the lithography process at all of the set of processconditions to produce a single model, wherein the single model is usedto model the lithography process at the nominal condition.
 47. Themethod of claim 46, wherein the at least one parameter is focus.
 48. Themethod of claim 46, wherein the at least one parameter includes one ormore of illumination source, numerical aperture, and opticalaberrations.
 49. The method of claim 46, further comprising: selecting aset of test patterns for a test mask; printing the set of test patternson a wafer to form a set of test structures; and using the set of teststructures to produce the actual results.
 50. The method of claim 46,wherein calibrating the model further comprises: comparing the simulatedresults with actual results of the lithography process at all of the setof process conditions to produce the total difference measure betweenthe simulated results and the actual results; modifying values of thefitting parameters and generating additional simulated results at theset of process conditions to identify optimum fitting parameter valuessuch that the total difference measure between the actual results andsimulated results produced using the optimum fitting parameter values isminimized or is below a predetermined threshold; and defining the singlemodel as the model including the optimum fitting parameter values. 51.The method of claim 46, wherein the total difference measure is a rootmean square difference.
 52. The method of claim 46, wherein the set ofprocess conditions includes only process conditions at nominal exposureand varying values of focus.
 53. The method of claim 46, wherein the setof process conditions includes only nominal condition, a positivedefocus condition at nominal exposure condition, and a negative defocuscondition at nominal exposure condition.
 54. A computer-readable mediumstoring instructions for causing a computer to create a focus-exposuremodel of a lithography process by performing: storing a model of alithography process, the model including an optical model module, themodel having a set of model parameters including focus, exposure, and aset of fitting parameters having variable values; storing a set ofinitial fitting parameter values for the model; storing a plurality ofsampling locations within a process window in a focus-exposure space,the plurality of sampling locations including nominal condition andbeing a subset of all possible process conditions within the processwindow; generating simulated results of the lithography process at eachof the plurality of sampling locations using the model with the set ofinitial fitting parameter values by simulating the lithography processwith varying values of focus and exposure corresponding to the pluralityof sampling locations while holding the initial fitting parameter valuesconstant; comparing the simulated results with actual results of thelithography process at each of the plurality of sampling locations toproduce a total difference measure between the simulated results and theactual results at all of the plurality of sampling locations; modifyingthe set of fitting parameter values and generating additional simulatedresults at each of the plurality of sampling locations such that thetotal difference measure between the actual results produced using theoptimum fitting parameter values is minimized or is below apredetermined threshold; and defining the focus-exposure model as themodel including the optimum fitting parameter values, the focus-exposuremodel being capable of simulating the lithography process at anylocation within the process window.
 55. The computer-readable medium ofclaim 54, wherein the set of model parameters further includes one ormore first principle parameters in addition to focus and exposure. 56.The computer-readable medium of claim 55, wherein the one or more firstprinciple parameters includes one or more of illumination source,numerical aperture, and optical aberrations.
 57. The computer-readablemedium of claim 54, wherein the model of the lithography process furtherincludes a resist model module.
 58. The computer-readable medium ofclaim 54, wherein the model of the lithography process further includesa mask model module.
 59. The computer-readable medium of claim 54,wherein the plurality of sampling locations includes only samplinglocations at nominal exposure and varying values of focus.
 60. Thecomputer-readable medium of claim 54, wherein the plurality of samplinglocations includes only nominal condition, a positive defocus conditionat nominal exposure condition, and a negative defocus condition atnominal exposure condition.
 61. The computer-readable medium of claim54, wherein the total difference measure is a root mean squaredifference.